The number and types of devices that collect data about individuals and households continues to grow. Many users now have desktop computers, laptops, tablets, gaming devices, cell phones, and/or home appliances such as refrigerators, thermostats, and toasters, that are configured with monitoring and information tracking capabilities. The increase in the number and types of such devices is making a massive amount of disparate (e.g., device specific) data points available.
Given such a massive amount of data, it is desirable to determine a set of devices that are associated with a particular user profile for an individual or household so that, when actions on those devices are tracked, the actions can be associated with a particular user profile and collectively used, for example, to identify and provide targeted marketing and content to the devices associated with the user profile. However, identifying a set of devices associated with a particular user profile is often difficult because individuals and households commonly have multiple devices, share devices with other users, borrow devices from one another, and use public-access devices. Existing techniques that group devices by making probabilistic determinations based on common device IP addresses generally lack accuracy. In addition, such probabilistic matching and other grouping techniques are applied on remote server devices that must attempt to process the massive and ever growing amount of data. Such techniques are highly complex, non-deterministic with any amount of consistency, and require significant, and possibly prohibitive, amounts of data storage and processing capabilities.